Everest Group IDP
             PEAK Matrix® 2022  
Indico Named as Major Contender and Star Performer in Everest Group's PEAK Matrix® for Intelligent Document Processing (IDP)
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Intelligent Document Processing (IDP): How to Convert Unstructured Data with Automation

How IDP Automates the Transformation of Your Unstructured Data to Structured Data

Extract unstructured data from unstructured documents in order to deliver end-to-end process automation

Download the eBook: The Unstructured Data Imperative. Why Enterprises need to act now.

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Key benefits of Intelligent Document Processing

Indico = AI success

At least 89% of AI projects fail to deliver, according to industry estimates.
But not with Indico: Our customers report a 94% success rate for production projects

Rapid time to value

Build high-quality custom machine learning models with as few as 200 labeled examples – a fraction of the data traditional AI solutions require

Simple to deploy and use

Accurately capture and validate real world data through an intuitive user interface and machine teaching

85% process cycle time reduction

Realize faster time to market for new initiatives and improve customer satisfaction

4x increase in process capacity

Create dramatic cost efficiencies for back-office functions and scale critical processes without increasing expenses

Highly scalable

A single platform addresses numerous unstructured content use cases across the enterprise – deployed by your process experts, not IT or data scientists

Bringing Value to Stakeholders Throughout the Enterprise

AI & IA professionals

 

  • Automate complex document-based workflows at scale without templates or rule engines.
  • Achieve explainable AI – understand how decisions are made.
  • Reduce training data by 1000x with transfer learning.

 

 

Process professionals

 

  • Automate human-like decision-making at scale.
  • Lower costs by creating more efficient processes.
  • Realize up to 4x increase in workflow capacity.

 

What is Intelligent Document Processing?

Empowering businesses to automate the most complex document-based workflows

Intelligent document processing enables businesses to automate processes which include unstructured data, and without huge data sets that are normally required to accurately train automation models.

Instead, an intelligent document processing platform is based on a massive, pre-built database of labeled data points. The platform then incorporates transfer learning, an artificial intelligence technology, which enables a model trained on one task to be used for a related task. Transfer learning obviates the need for a model to be trained on thousands of documents in order to achieve accuracy.

Indico, for example, has a base model consisting of some 500 million labeled data points. And thanks to transfer learning, it takes only about 200 documents and a few hours to train our document process automation model with about 95% accuracy. That reduces the underlying data required by a factor of 100 to 1000x as compared to traditional approaches.

That dramatic reduction in data required also means the Indico platform doesn’t require massive amounts of computing power, like many automated document processing technologies do. Rather, it can run effectively on just one or two GPUs, and scale from there using low-cost CPU.

Intelligent document automation for processing has gained traction at financial institutions, insurance companies, commercial real estate firms and other companies that have reached the limit of what they can do with robotic process automation (RPA). These firms are looking to take the next step in their document processing automation journey when it comes to unstructured data (see our automated document processing use cases section below).

While RPA and optical character recognition (OCR) templated approaches to document process automation work well with highly structured data, where the expected data is in the same place every time, RPA and OCR cannot handle unstructured data – such as email, Word documents, images, PDFs and more. It’s an important distinction because in most organizations, at least 80% of their data is unstructured, which first-generation automation solutions have difficulty dealing with.

Intelligent document processing vs. RPA

It’s important to delve deeply into how each automated document processing solution for business works in order to understand its true capabilities. Today, many vendors are playing fast and loose with all kinds of AI-related terms, including IDP. You may come across RPA vendors who say they incorporate AI document processing technology to deliver what sounds like IDP. Under the covers, you will likely find the solution is merely a rules engine at heart that can’t handle unstructured data. In practice, “RPA + AI” solutions solve only for structured data, or, at best, semi-structured data, such as some invoices.

A true intelligent document processing solution needs to be able to incorporate document process automation that will handle any type of data: structured, semi-structured and unstructured. Given that the vast majority of data in your company is likely unstructured, only a solution that can effectively handle it will be able to accelerate your intelligent document automation processing and deliver transformative change. Don’t settle for less.


 

Automate complex business processes without data science expertise

Indico’s intelligent document processing solution makes it easy to build models that automate document-intensive processes normally performed by humans.

Most processes require humans to read documents, find appropriate data and enter it into a downstream system. With Indico, the business subject matter experts who understand the processes best build models to automate such processes.

Using Indico’s intuitive tools, process experts label the data points they want to extract from documents. As they do so, the model updates in real time to show predictions on how well the model will perform the task at hand. When the prediction hits an acceptable level, you’re done. Typically, it takes a few dozen to maybe 200 documents to properly train a model.

Having the people who understand the business process and the desired results actually build the models is a crucial component of the Indico approach. It turns these business people into “citizen data scientists” – even though they don’t need any data science expertise. It’s a far more rapid and accurate approach than having business people try to explain a process to a data scientist, who then goes off and builds a model. With Indico, there’s no risk of requirements being lost in translation.

All Indico tools are in plain English and are simple to use. Fully working models can be built in as little as an hour.


 

Sound IPD requires cognitive artificial intelligence technology

While Indico’s platform is simple to use, it’s built on some sophisticated cognitive AI technology that we keep behind the scenes.

One example is deep learning. Normally, users have to program a model such that the computer can understand what it needs to do. Deep learning turns that notion around and says, “Show me examples of what you want to achieve and I’ll figure out how to do it.”

Natural language processing (NLP) is likewise a crucial element. NLP enables the Indico platform to understand context in a given piece of data– whether structured or unstructured. It enables the model to “read” a document and understand it just as a human would. But it functions strictly behind the scenes; there’s no need for business users to understand what NLP is or how it works.

That goes for machine learning (ML) as well. The Indico platform is built on sophisticated ML models, but users never have to interact with them. They simply focus on delivering business benefits by building models that remove repetition and complexity from document-intensive processes, while improving accuracy.

You may find other products that incorporate deep learning, NLP and ML to address processes that involve unstructured data, but you will likely find them to be far more complex to implement. Typically they do require data science expertise, along with millions of dollars to implement and maintain.

Keys to Success with Intelligent Document Processing

To create effective automation models on an enterprise scale, an intelligent document processing platform needs to support several key features, including:

Staggered Loop approach to continual learning

As data passes through your production models, your process experts can offer corrections and selectively dictate which ones should be incorporated into the model.

Human in the loop

Human in the loop is similar to staggered loop but can be configured such that any updated example employees submit is incorporated into the model, turning model updates into a highly-transparent, push-button process.

Explainable AI

Whether for regulatory compliance or just peace of mind, you want to be able to explain how and why your automation models make decisions. Too often, AI engines make decisions in a black box, giving you no visibility into the rationale behind them.

Your automation platform should make it simple for anyone – from business process experts to auditors – to determine why a model made the decision it did.

Testing and benchmarking

You shouldn’t have to recreate the wheel when developing automation models. A sound intelligent document processing platform will give you access to hundreds of custom tasks, tens of billions of words, terabytes of images and bleeding-edge research that you can incorporate into your models. Access to open-source tools such as the Enso project is also a plus, providing a standard interface for the benchmarking transfer learning methods for natural language processing tasks.

Intelligent Document Processing use cases

Mortgage underwriting

The mortgage underwriting process typically involves humans looking over lots of documents to assess an applicant’s creditworthiness. The IDP platform applies intelligent document automation to the processing of mortgage underwriting, “reading” the documents and extracting relevant data for input into the bank’s credit evaluation system.

AML Automation

Another common use case for commercial banking automation is meeting regulatory requirements around anti-money laundering (AML).In the U.S., that means complying with the Bank Secrecy Act and related regulations meant to deter money laundering by terrorist networks and drug cartels.

 

Customer onboarding

IDP can take the various documents required to onboard a new customer and automatically classify them, extract relevant data and input it into the bank’s digital management system. Customers are onboarded more quickly, with increased accuracy, resulting in faster time to revenue for the bank and improved customer satisfaction.

KYC process automation

Closely related to AML requirements is “know your customer” regulations, and they present similar challenges. As part of the commercial banking client onboarding process, these laws require banks to make an effort to verify the identity of customers as well as the risks involved in any business relationship with them.

Automate LIBOR document processing

The LIBOR interest rate benchmark was phased out at the end of 2021. Banks and financial institutions worldwide are left poring through documents looking for references to LIBOR in order to determine what their exposure is and take steps to address it – a task that screams for IDP.

Life insurance underwriting

Applying intelligent document processing to life insurance underwriting can help companies dramatically improve the process by largely taking humans out of the equation. With IDP, companies can create models to quickly categorize and extract data from reams of documents.

Claims processing

For insurance claims processing, intelligent document processing can be used to automate the classification and annotation of new claims, and route them to the appropriate subject matter expert for processing. It can also help extract pertinent information from documents, including unstructured data such as images and free-form notes from insurance adjusters.

Financial document analysis

Investment banking firms and others with wealth management divisions can take advantage of financial services automation by using it to analyze financial documents. Normally, humans read financial statements and pore over investment data, manually extracting relevant data. IDP enables financial firms to automate the process, pulling out relevant data and normalizing it for insertion into data processing tools. The result is a dramatic improvement in speed, efficiency and accuracy.

Trade processing automation

Investment firms often receive trade processing documents via email and in PDFs. They can use intelligent data processing tools to automate trade processing by extracting relevant unstructured data from these documents, and normalizing it for input into the firm’s digital management system, eliminating hours and hours of manual data processing.

Long-term healthcare invoices

While many consider invoices to be structured or semi-structured documents, given the variation in invoices from different companies, they really fall into the unstructured category. Nowhere is that more true than with respect to invoices from long-term healthcare facilities, where each provider uses its own format and lists numerous services provided. But customers are successfully using intelligent document processing to pull more than a dozen fields from these invoices and convert them to a structured format – a task no RPA or templated automation tool could handle.

Call center transcript analysis

Companies have long had the ability to transcribe call center conversations into text, but pulling out any actionable intelligence required employees to painstakingly read the transcripts. Intelligent document processing offers a way to automate call center transcript analysis. With its ability to “read” unstructured documents, an IDP model can be trained to analyze the text, looking for key words and phrases that indicate a caller may be amenable to buying additional goods or services, or is unhappy and in danger of jumping ship. Either way, that’s actionable intelligence that can deliver real business benefits.

Automating insurance risk reduction

Insurance is all about risk, and insurance companies are always looking for ways to reduce their own risk. Now some are getting creative, applying intelligent document processing to examine documents that may hold the keys to risk reduction. One company built an automation model to examine some 180,000 documents to find those that could help it more accurately price its workers’ compensation insurance policies. It took just one month to examine all the documents, a job that would’ve taken about 2.5 years for a single employee to accomplish. Others are using intelligent automation to increase accuracy in customer on-boarding, to reduce their risk of legal and regulatory mandate violations.

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Benefits of Intelligent Document Processing

Bringing transformational efficiencies to the enterprise

Use cases like those above make it easy to see how IDP saves companies time and money. From our experience with customers, here’s the kind of gains you can expect from Indico:

85% reduction in process cycle times

Realize faster time to market for new initiatives and improve customer satisfaction

4x increase in process capacity

Create dramatic cost efficiencies for back-office functions and scale critical processes without increasing expenses

80% reduction in human resources

Free up employees from tedious, low-value tasks and repurpose them for higher value, more strategic projects

Ease of use

With no data science expertise required, turn your business process experts into citizen data scientists

1000x less training data required

Build effective models with a fraction of the data traditional artificial intelligence solutions require

Built for unstructured data

Automate your most complex document-based workflows

 

Case Studies

Cushman & Wakefield Saves 16,000 Staff Hours with Intelligent Document Processing

The commercial real estate giant Cushman & Wakefield chose the Indico Unstructured Platform for intelligent document processing based on four differentiators:

  • Its intuitive interface enables business process experts to build their own automation models
  • Process experts own their models and can modify them as needed – without assistance from IT or data scientists
  • Analytics capabilities identify relevant terms in a document, even if they don’t appear in the same place each time
  • The Indico Data platform is flexible, able to apply to numerous use cases and document types across business units

A single automation initiative is saving Cushman & Wakefield at least 16,000 employee hours while also speeding turnaround time by 70%.

Read the full case study here.


Intelligent Automation Enables Chatham Financial to Reduce Costs and Increase Process Capacity

As the largest independent financial risk management advisory and technology firm, Chatham Financial serves more than 3,000 companies and handles more than $750 billion in transaction volume annually.

After a thorough proof of concept (POC) project, Chatham chose Indico Data to help it automate the processing of tens of thousands of complex, unstructured documents. Among the results:

  • For the initial POC use case, the company reduced costs by 75% while increasing process capacity by 4x
  • In 24 hours, Chatham cleared a backlog of 1,000 documents that had built up over years
  • Five intelligent document processing use cases are now in production, with five more on tap
  • Chatham now has 50 internal business users, or “citizen data scientists,” building automation models with the Indico Data platform

Read the full case study here.


Cognizant Delivers Millions in Savings for Real Estate Data Provider

When the global IT systems integrator and consulting firm Cognizant was searching for a solution to help a client automate processing unstructured mortgage title and deed documents, it turned to Indico Data. Cognizant helped the client achieve impressive results, including:

  • Automated processing of some 40 million documents per year
  • 40% reduction in processing costs, dramatically reducing reliance on thousands of offshore data entry resources
  • With just 200 sample documents, process owners – not data scientists – trained models to extract more than 100 fields from unstructured documents
  • Staggered loop approach enables process experts to constantly improve model accuracy

Read the full case study here.


Insurance Giant Finds $100 Million in Savings with Intelligent Document Processing Insurance Giant Hits RPA Wall, Finds $100M in Savings with Intelligent Document Processing

A 150+ year-old Fortune 50 insurance company turned to Indico after it hit a wall with its robotic process automation (RPA) platform, which couldn’t process documents containing unstructured data. After a bakeoff, the company settled on Indico, in part on the strength of its intuitive user interface, which enabled “just about anybody” to build automation models.

The company’s “citizen data scientists” have created models to address use cases including:

  • Workers’ compensation policies, which enabled the company to analyze 134,000 documents in a matter of days
  • Benefits contract analysis, which involves extracting insights from contracts of up 100 pages in length, freeing up highly experienced staff for more valuable, rewarding work
  • Automating processing of documents involved in customer onboarding
  • Triaging emails involved in claims processing, including identifying the appropriate claims adjuster and offering up suggested answers to customer queries

“We’ve identified almost $100 million in hard value, hard dollar savings, that we can quite easily achieve over the next three to four years using unstructured data digitization and analytics tools,” said the company’s the VP of Strategy and Planning.

Read the full case study here.

Calculating the ROI of Intelligent Document Processing

Increase efficiency, reduce costs, transform the enterprise

Perhaps figures like 85% reduction in cycle times and 4x increase in process capacity sound almost too good to be true. We can assure you they are most certainly real and add up to a rapid return-on-investment (ROI).

Consider a document-intensive process that involves 10 employees who each earn $100,000 per year, or $1 million total. Let’s say the team performs 500,000 tasks per year for a given process; that comes to $2 per task. If an IDP solution can automate 75% of those tasks – a perfectly reachable goal – the cost per task falls to just 50 cents, so the company saves $750,000 per year.

Looking at it another way, you now have $750,000-worth of employee hours to dedicate to other areas – a dramatic increase in overall capacity.

At the same time, by freeing up employees from tedious tasks, you gain soft benefits including increased employee satisfaction and productivity. Meanwhile, the IDP solution will perform the newly automated tasks with increased accuracy and consistency – because computers don’t get tired or make typos.

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Resources

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Answers to the most complex questions in unstructured data.

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Unstructured Unlocked

Enterprise leaders discuss how to unlock value from unstructured data.